Improved Detection of Common Variants Associated with Schizophrenia and Bipolar Disorder Using Pleiotropy-Informed Conditional False Discovery Rate

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Several lines of evidence suggest that genome-wide association studies (GWAS) have the potential to explain more of the “missing heritability” of common complex phenotypes. However, reliable methods to identify a larger proportion of single nucleotide polymorphisms (SNPs) that impact disease risk are currently lacking. Here, we use a genetic pleiotropy-informed conditional false discovery rate (FDR) method on GWAS summary statistics data to identify new loci associated with schizophrenia (SCZ) and bipolar disorders (BD), two highly heritable disorders with significant missing heritability. Epidemiological and clinical evidence suggest similar disease characteristics and overlapping genes between SCZ and BD. Here, we computed conditional Q–Q curves of data from the Psychiatric Genome Consortium (SCZ; n = 9,379 cases and n = 7,736 controls; BD: n = 6,990 cases and n = 4,820 controls) to show enrichment of SNPs associated with SCZ as a function of association with BD and vice versa with a corresponding reduction in FDR. Applying the conditional FDR method, we identified 58 loci associated with SCZ and 35 loci associated with BD below the conditional FDR level of 0.05. Of these, 14 loci were associated with both SCZ and BD (conjunction FDR). Together, these findings show the feasibility of genetic pleiotropy-informed methods to improve gene discovery in SCZ and BD and indicate overlapping genetic mechanisms between these two disorders.

Vyšlo v časopise: Improved Detection of Common Variants Associated with Schizophrenia and Bipolar Disorder Using Pleiotropy-Informed Conditional False Discovery Rate. PLoS Genet 9(4): e32767. doi:10.1371/journal.pgen.1003455
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pgen.1003455

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Zdroje

1. GlazierAM, NadeauJH, AitmanTJ (2002) Finding genes that underlie complex traits. Science 298: 2345–2349.

2. HindorffLA, SethupathyP, JunkinsHA, RamosEM, MehtaJP, et al. (2009) Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc Natl Acad Sci U S A 106: 9362–9367.

3. HirschhornJN, DalyMJ (2005) Genome-wide association studies for common diseases and complex traits. Nat Rev Genet 6: 95–108.

4. YangJ, ManolioTA, PasqualeLR, BoerwinkleE, CaporasoN, et al. (2011) Genome partitioning of genetic variation for complex traits using common SNPs. Nat Genet 43: 519–525.

5. YooYJ, PinnaduwageD, WaggottD, BullSB, SunL (2009) Genome-wide association analyses of North American Rheumatoid Arthritis Consortium and Framingham Heart Study data utilizing genome-wide linkage results. BMC Proc 3 Suppl 7: S103.

6. LeeSH, DeCandiaTR, RipkeS, YangJ, SullivanPF, et al. (2012) Estimating the proportion of variation in susceptibility to schizophrenia captured by common SNPs. Nat Genet 44: 247–250.

7. StahlEA, WegmannD, TrynkaG, Gutierrez-AchuryJ, DoR, et al. (2012) Bayesian inference analyses of the polygenic architecture of rheumatoid arthritis. Nat Genet 44: 483–489.

8. ManolioTA, CollinsFS, CoxNJ, GoldsteinDB, HindorffLA, et al. (2009) Finding the missing heritability of complex diseases. Nature 461: 747–753.

9. SivakumaranS, AgakovF, TheodoratouE, PrendergastJG, ZgagaL, et al. (2011) Abundant pleiotropy in human complex diseases and traits. Am J Hum Genet 89: 607–618.

10. WagnerGP, ZhangJ (2011) The pleiotropic structure of the genotype-phenotype map: the evolvability of complex organisms. Nat Rev Genet 12: 204–213.

11. ChambersJC, ZhangW, SehmiJ, LiX, WassMN, et al. (2011) Genome-wide association study identifies loci influencing concentrations of liver enzymes in plasma. Nat Genet 43: 1131–1138.

12. CotsapasC, VoightBF, RossinE, LageK, NealeBM, et al. (2011) Pervasive sharing of genetic effects in autoimmune disease. PLoS Genet 7: e1002254 doi:10.1371/journal.pgen.1002254.

13. RipkeS, SandersAR, KendlerKS, LevinsonDF, SklarP, et al. (2011) Genome-wide association study identifies five new schizophrenia loci. Nat Genet 43: 969–976.

14. SklarP, RipkeS, ScottLJ, AndreassenOA, CichonS, et al. (2011) Large-scale genome-wide association analysis of bipolar disorder identifies a new susceptibility locus near ODZ4. Nat Genet 43: 977–983.

15. LichtensteinP, YipBH, BjorkC, PawitanY, CannonTD, et al. (2009) Common genetic determinants of schizophrenia and bipolar disorder in Swedish families: a population-based study. Lancet 373: 234–239.

16. PurcellSM, WrayNR, StoneJL, VisscherPM, O'DonovanMC, et al. (2009) Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature 460: 748–752.

17. StefanssonH, OphoffRA, SteinbergS, AndreassenOA, CichonS, et al. (2009) Common variants conferring risk of schizophrenia. Nature 460: 744–747.

18. CraddockN, OwenMJ (2007) Rethinking psychosis: the disadvantages of a dichotomous classification now outweigh the advantages. World Psychiatry 6: 84–91.

19. VietaE, PhillipsML (2007) Deconstructing bipolar disorder: a critical review of its diagnostic validity and a proposal for DSM-V and ICD-11. Schizophr Bull 33: 886–892.

20. FischerBA, CarpenterWTJr (2009) Will the Kraepelinian dichotomy survive DSM-V? Neuropsychopharmacology 34: 2081–2087.

21. SimonsenC, SundetK, VaskinnA, BirkenaesAB, EnghJA, et al. (2011) Neurocognitive dysfunction in bipolar and schizophrenia spectrum disorders depends on history of psychosis rather than diagnostic group. Schizophr Bull 37: 73–83.

22. CrowTJ (1986) The continuum of psychosis and its implication for the structure of the gene. Br J Psychiatry 149: 419–429.

23. CraddockN, OwenMJ (2005) The beginning of the end for the Kraepelinian dichotomy. Br J Psychiatry 186: 364–366.

24. CraddockN, O'DonovanMC, OwenMJ (2009) Psychosis genetics: modeling the relationship between schizophrenia, bipolar disorder, and mixed (or “schizoaffective”) psychoses. Schizophr Bull 35: 482–490.

25. O'DonovanMC, CraddockN, NortonN, WilliamsH, PeirceT, et al. (2008) Identification of loci associated with schizophrenia by genome-wide association and follow-up. Nat Genet 40: 1053–1055.

26. WilliamsHJ, CraddockN, RussoG, HamshereML, MoskvinaV, et al. (2011) Most genome-wide significant susceptibility loci for schizophrenia and bipolar disorder reported to date cross-traditional diagnostic boundaries. Hum Mol Genet 20: 387–391.

27. Efron B (2010) Large-scale inference : empirical Bayes methods for estimation, testing, and prediction. Cambridge ; New York: Cambridge University Press. xii: , 263 p. p.

28. SchwederT, SpjotvollE (1982) Plots of P-Values to Evaluate Many Tests Simultaneously. Biometrika 69: 493–502.

29. BenjaminiY, HochbergY (1995) Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society Series B (Methodological): Blackwell Publishing 289–300.

30. SteinbergS, de JongS, AndreassenOA, WergeT, BorglumAD, et al. (2011) Common variants at VRK2 and TCF4 conferring risk of schizophrenia. Hum Mol Genet 20: 4076–4081.

31. FerreiraMA, O'DonovanMC, MengYA, JonesIR, RuderferDM, et al. (2008) Collaborative genome-wide association analysis supports a role for ANK3 and CACNA1C in bipolar disorder. Nat Genet 40: 1056–1058.

32. GreenEK, GrozevaD, FortyL, Gordon-SmithK, RussellE, et al. (2012) Association at SYNE1 in both bipolar disorder and recurrent major depression. Mol Psychiatry doi: 10.1038/mp.2012.48.

33. EfronB (2007) Size, power and false discovery rates. The Annals of Statistics 35: 1351–1377.

34. YangJ, WeedonMN, PurcellS, LettreG, EstradaK, et al. (2011) Genomic inflation factors under polygenic inheritance. Eur J Hum Genet 19: 807–812.

35. EfronB, TibshiraniR (2002) Empirical bayes methods and false discovery rates for microarrays. Genet Epidemiol 23: 70–86.

36. AndreassenOA, DjurovicS, ThompsonWK, SchorkAJ, KendlerKS, et al. (2013) Improved Detection of Common Variants Associated with Schizophrenia by Leveraging Pleiotropy with Cardiovascular-Disease Risk Factors. Am J Hum Genet 7;92 (2)

197–209 doi: 10.1016/j.ajhg.2013.01.001.

37. SunL, CraiuRV, PatersonAD, BullSB (2006) Stratified false discovery control for large-scale hypothesis testing with application to genome-wide association studies. Genet Epidemiol 30: 519–530.

38. ChenDT, JiangX, AkulaN, ShugartYY, WendlandJR, et al. (2011) Genome-wide association study meta-analysis of European and Asian-ancestry samples identifies three novel loci associated with bipolar disorder. Mol Psychiatry

39. Detera-WadleighSD, McMahonFJ (2006) G72/G30 in schizophrenia and bipolar disorder: review and meta-analysis. Biol Psychiatry 60: 106–114.

40. DiesetI, DjurovicS, TesliM, HopeS, MattingsdalM, et al. (2012) NOTCH4 Gene Expression is Upregulated in Bipolar Disorder. Am J Psychiatry in press

41. LarkumME, NevianT, SandlerM, PolskyA, SchillerJ (2009) Synaptic integration in tuft dendrites of layer 5 pyramidal neurons: a new unifying principle. Science 325: 756–760.

42. PollardKS, SalamaSR, LambertN, LambotMA, CoppensS, et al. (2006) An RNA gene expressed during cortical development evolved rapidly in humans. Nature 443: 167–172.

43. DevlinB, RoederK (1999) Genomic control for association studies. Biometrics 55: 997–1004.

44. StoreyJD (2003) The positive false discovery rate: A Bayesian interpretation and the q-value. Annals of Statistics 2013–2035.

45. NicholsT, BrettM, AnderssonJ, WagerT, PolineJB (2005) Valid conjunction inference with the minimum statistic. Neuroimage 25: 653–660.